{"id":"W2765134536","doi":"10.1101/203554","title":"Genomics in healthcare: GA4GH looks to 2022","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Genomics; Health care; Context (archaeology); Big data; Data science; Preparedness; Business; Genome; Political science; Computer science; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05571505,0.0008691036,0.0008002811,0.001148311,0.002242595,0.01190067,0.002307017,0.01568094,0.01867147],"category_scores_gemma":[0.06938101,0.000427641,0.001330727,0.001204021,0.007274659,0.01053543,0.007887443,0.01451784,0.006911313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005096263,"about_ca_system_score_gemma":0.01406082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00591816,"about_ca_topic_score_gemma":0.005331267,"domain_scores_codex":[0.978843,0.01392494,0.0006040142,0.001182393,0.003241139,0.002204648],"domain_scores_gemma":[0.9705709,0.0145138,0.001091917,0.002376847,0.004562505,0.006884084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003439628,0.00008475511,0.002762993,0.0001812147,0.00007068708,0.0001661965,0.0003559969,0.001592881,0.0003080817,0.3128601,0.6170179,0.06425518],"study_design_scores_gemma":[0.0001677477,0.000155328,0.002843992,0.0007133962,0.00004166312,0.000183208,0.0008273714,0.002012565,0.0004213492,0.3339113,0.6586242,0.0000978546],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001226376,0.002605859,0.003592344,0.9744505,0.006053296,0.00002793085,0.0005055404,0.0001803332,0.01135789],"genre_scores_gemma":[0.1011591,0.005170322,0.01871658,0.8443433,0.01171224,0.0003324228,0.001856816,0.0004196833,0.01628954],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.05571505,"threshold_uncertainty_score":0.2946528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1830240817469813,"score_gpt":0.4381929078838762,"score_spread":0.2551688261368949,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}